End-to-End Symmetry Preserving Inter-Atomic Potential Energy Model for Finite and Extended Systems
Abstract
Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.
Cite
Text
Zhang et al. "End-to-End Symmetry Preserving Inter-Atomic Potential Energy Model for Finite and Extended Systems." Neural Information Processing Systems, 2018.Markdown
[Zhang et al. "End-to-End Symmetry Preserving Inter-Atomic Potential Energy Model for Finite and Extended Systems." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/zhang2018neurips-endtoend/)BibTeX
@inproceedings{zhang2018neurips-endtoend,
title = {{End-to-End Symmetry Preserving Inter-Atomic Potential Energy Model for Finite and Extended Systems}},
author = {Zhang, Linfeng and Han, Jiequn and Wang, Han and Saidi, Wissam and Car, Roberto and E, Weinan},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {4436-4446},
url = {https://mlanthology.org/neurips/2018/zhang2018neurips-endtoend/}
}